Convolutions, Blurring and Sharpening Images

In this lesson we'll learn:

1. Convolution Operations
2. Blurring
3. Denoising
4. Sharpening

# Our Setup, Import Libaries, Create our Imshow Function and Download our Images
import cv2
import numpy as np
from matplotlib import pyplot as plt

# Define our imshow function 
def imshow(title = "Image", image = None, size = 10):
    w, h = image.shape[0], image.shape[1]
    aspect_ratio = w/h
    plt.figure(figsize=(size * aspect_ratio,size))
    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    plt.title(title)
    plt.show()

# # Download and unzip our images
# !wget https://moderncomputervision.s3.eu-west-2.amazonaws.com/images.zip
# !unzip -qq images.zip

1 - Blurring using Convolutions

import cv2
import numpy as np

image = cv2.imread('Datasets/images/flowers.jpeg')
imshow('Original Image', image)

# 3 x 3 kernel qora kernel yaratish. 
#kernel kopaytmasiga bolish orqali yorug'lik qiymatini saqlab qolish aks holda yoruglik kopayib oqarib ketadi
kernel_3x3 = np.ones((3, 3), np.float32) / 9 
kernel_3x3_2 = np.ones((3, 3), np.float32)

# cv2.fitler2D - kernelni rasmga apply qilish uchun
blurred = cv2.filter2D(image, -1, kernel_3x3) 
imshow('3x3 Kernel Blurring', blurred)

# Kernelni yorugligini pasaytirmasdan bolish
blurred2 = cv2.filter2D(image, -1, kernel_3x3_2) 
imshow('3x3 without deviding kernel ', blurred2)


# Kattaroq matrix 7 x 7 kernel. Ta'siri ko'proq bo'lishi uchun
kernel_7x7 = np.ones((7, 7), np.float32) /49
blurred2 = cv2.filter2D(image, -1, kernel_7x7)
imshow('7x7 Kernel Blurring', blurred2)

Other commonly used blurring methods in OpenCV

• Regular Blurring
• Gaussian Blurring
• Median Blurring

import cv2
import numpy as np

image = cv2.imread('Datasets/images/flowers.jpeg')

# Averaging done by convolving the image with a normalized box filter. 
# This takes the pixels under the box and replaces the central element
# Box size needs to odd and positive 
blur = cv2.blur(image, ksize=(5,5))
imshow('Averaging', blur)

# Instead of box filter, gaussian kernel
Gaussian = cv2.GaussianBlur(image, (5,5), 0)
imshow('Gaussian Blurring', Gaussian)

# Takes median of all the pixels under kernel area and central 
# element is replaced with this median value
median = cv2.medianBlur(image, 5)
imshow('Median Blurring', median)

Bilateral Filter - Noise removal filter

dst = cv.bilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]])

• src Source 8-bit or floating-point, 1-channel or 3-channel image.
• dst Destination image of the same size and type as src .
• d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
• sigmaColor Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.
• sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
• borderType border mode used to extrapolate pixels outside of the image

# Bilateral is very effective in noise removal while keeping edges sharp
bilateral = cv2.bilateralFilter(image, 9, 75, 75)
imshow('Bilateral Blurring', bilateral)

3 Image De-noising - Non-Local Means Denoising

There are 4 variations of Non-Local Means Denoising:

• cv2.fastNlMeansDenoising() - works with a single grayscale images
• cv2.fastNlMeansDenoisingColored() - works with a color image.
• cv2.fastNlMeansDenoisingMulti() - works with image sequence captured in short period of time (grayscale images)
• cv2.fastNlMeansDenoisingColoredMulti() - same as above, but for color images.

fastNlMeansDenoisingColored(InputArray src, OutputArray dst, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )¶

Parameters for fastNlMeansDenoisingColored:

• src – Input 8-bit 3-channel image.
• dst – Output image with the same size and type as src . templateWindowSize – Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
• searchWindowSize – Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
• h – Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
• hColor – The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors

image = cv2.imread('Datasets/images/hilton.jpeg')
imshow('Original', image)

dst = cv2.fastNlMeansDenoisingColored(image, None, 6, 6, 7, 21)
imshow('fastNlMeansDenoisingColored', dst)

4Sharpening Images - Rasm qirallarini chiqarish uchun.

# Loading our image
image = cv2.imread('Datasets/images/hilton.jpeg')
imshow('Original', image)

# Sharpening qiladigon kernel, Kernel matrix ichidagi sonlar yig'indisi 1 ga teng bo'lishi kerak 
kernel_sharpening = np.array([[-1,-1,-1], 
                              [-1, 9,-1], #center ga yoziladi asosiy kernel raqami
                              [-1,-1,-1]])

# applying the sharpening kernel to the image
sharpened = cv2.filter2D(image, -1, kernel_sharpening)  #cv2.filter2D funksiyasi orqali amalga oshiriladi
imshow('Sharpened Image', sharpened)